Cantera Ignition Delay
K-Dense-AI/scientific-agent-skills
Runs Cantera constant-volume or constant-pressure ignition simulations and reports temperature-based ignition delay with mechanism provenance and checks.
Cross-driver GUI actuation for CAE solvers running under sim-cli.
$ npx skills add svd-ai-lab/sim-cli --skill gui -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install svd-ai-lab/sim-cli gui --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/svd-ai-lab/sim-cli.git skills-src && mkdir -p .claude/skills && cp -r skills-src/src/sim/_skills/sim-cli/gui .claude/skills/gui && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "gui" agent skill from https://github.com/svd-ai-lab/sim-cli/tree/main/src/sim/_skills/sim-cli/gui into .claude/skills/gui/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gui", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/svd-ai-lab/sim-cli/tree/main/src/sim/_skills/sim-cli/guiType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add svd-ai-lab/sim-cli --skill gui -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install svd-ai-lab/sim-cli gui --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/svd-ai-lab/sim-cli.git skills-src && mkdir -p .agents/skills && cp -r skills-src/src/sim/_skills/sim-cli/gui .agents/skills/gui && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "gui" agent skill from https://github.com/svd-ai-lab/sim-cli/tree/main/src/sim/_skills/sim-cli/gui into .agents/skills/gui/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gui", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add svd-ai-lab/sim-cli --skill gui -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install svd-ai-lab/sim-cli gui --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/svd-ai-lab/sim-cli.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/src/sim/_skills/sim-cli/gui .cursor/skills/gui && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "gui" agent skill from https://github.com/svd-ai-lab/sim-cli/tree/main/src/sim/_skills/sim-cli/gui into .cursor/skills/gui/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gui", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/svd-ai-lab/sim-cli.git --path src/sim/_skills/sim-cli/gui--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add svd-ai-lab/sim-cli --skill gui -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install svd-ai-lab/sim-cli gui --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/svd-ai-lab/sim-cli.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/src/sim/_skills/sim-cli/gui .gemini/skills/gui && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "gui" agent skill from https://github.com/svd-ai-lab/sim-cli/tree/main/src/sim/_skills/sim-cli/gui into .gemini/skills/gui/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gui", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install svd-ai-lab/sim-cli guiInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add svd-ai-lab/sim-cli --skill gui -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/svd-ai-lab/sim-cli.git skills-src && mkdir -p .github/skills && cp -r skills-src/src/sim/_skills/sim-cli/gui .github/skills/gui && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "gui" agent skill from https://github.com/svd-ai-lab/sim-cli/tree/main/src/sim/_skills/sim-cli/gui into .github/skills/gui/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gui", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add svd-ai-lab/sim-cli --skill gui -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install svd-ai-lab/sim-cli gui --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/svd-ai-lab/sim-cli.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/src/sim/_skills/sim-cli/gui .opencode/skills/gui && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "gui" agent skill from https://github.com/svd-ai-lab/sim-cli/tree/main/src/sim/_skills/sim-cli/gui into .opencode/skills/gui/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gui", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
guiCross-driver GUI actuation for CAE solvers running under sim-cli.
Gui is an agent skill from svd-ai-lab/sim-cli. Cross-driver GUI actuation for CAE solvers running under sim-cli. Use to click buttons, fill fields, dismiss dialogs, and capture window screenshots against GUI-capable driver windows through sim exec.
Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `snippets/dismiss_named_dialog.py`).
It sits in Research & Science, covering Physical and earth sciences. The repository describes itself as: Turn existing CAE files into structured text an agent can use — COMSOL, Abaqus, Fluent, HFSS, Icepak, FloTHERM — plus solver detection, script linting, and live solver sessions… The licence is Apache-2.0.
3 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit b7318cc. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships script files (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Gui loads about 2.2k tokens when it runs. Until then it costs about 52 tokens; SKILL.md has 864 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from svd-ai-lab/sim-cli at commit b7318cc, republished under its Apache-2.0 licence (© svd-ai-lab). 864 words, ~2,228 tokens.
.claude/skills/gui/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.gui — cross-driver GUI actuationWhenever the active driver runs with ui_mode=gui (or desktop),
sim serve injects a gui object into your sim exec namespace
alongside session / solver / meshing / model. The object is the
same shape across solvers — only the process filter differs — so one
skill serves every GUI-capable driver.
/connect advertises it:
{
"ok": true,
"data": {
"...": "...",
"tools": ["gui"],
"tool_refs": {"gui": "sim-cli/_skills/sim-cli/gui/SKILL.md"}
}
}If tools doesn't contain "gui", the driver launched headless and
the object is absent — don't call it.
guiThree scenarios dominate:
gui.sim screenshot captures the
whole desktop. SimWindow.screenshot() captures just the window you
care about — cheaper to read, less visual clutter for the LLM.If the SDK has a programmatic path (session.tui.*, model.solve(),
ModelUtil.loadCopy()), prefer that. gui is for the UI-only surface
that the SDK doesn't cover.
gui is in the session namespace on the sim serve side. You talk to
it via the existing /exec HTTP channel:
# local Windows box
sim exec "dlg = gui.find('Login'); dlg.click('OK')"
# Windows box on the LAN / Tailscale
sim --host 10.0.x.y exec "dlg = gui.find('Login'); dlg.click('OK')"No new endpoint, no new protocol — the same API shape from anywhere the agent runs.
Requirement on the server host: sim serve must run in a real
interactive desktop session (normal login or RDP). Windows
service / SSH session 0 has no desktop, so pywinauto can't enumerate
any windows even though the solver processes are running. This is the
same constraint GUI-capable drivers document.
gui.available # True iff pywinauto can run — check before driving anything
gui.process_filter # tuple of process-name substrings this gui will target
gui.list_windows() # {ok, windows: [{hwnd, pid, proc, title, rect}, ...]}dlg = gui.find(title_contains="Login", timeout_s=5)
# returns a SimWindow, or None on timeouttitle_contains is a plain substring match (case-sensitive, any language).
Returns None if nothing matched — always check before calling
methods on it:
dlg = gui.find("连接到")
if dlg is None:
_result = {"ok": False, "error": "login dialog not visible"}
else:
dlg.click("确定")Every action returns {ok: bool, ...}. No exceptions unless you pass
invalid Python types — surface ok=False + error to the agent.
dlg.click("OK", timeout_s=5) # click a button by accessible name
dlg.send_text("alice", into="Username") # type into a named Edit field
dlg.send_text("/tmp/out.cas.h5") # without `into` → first editable
dlg.close() # WM_CLOSE (Alt+F4 equivalent)
dlg.activate() # bring to foreground
dlg.screenshot(label="after_login") # window-only PNG under workdirEach action method tries the most natural pywinauto strategy first
(button_by_title) and falls back to a broader match
(any_control_by_title) before giving up — the response tells you
which path worked via the strategy field.
Expensive but sometimes necessary for reasoning about an unfamiliar GUI:
state = gui.snapshot(max_depth=3)
# {ok, windows: [{hwnd, pid, proc, title,
# controls: [{name, control_type, handle, children?}, ...]}]}Use this when find(title) misses and you need to see what the GUI
actually exposes.
SimWindow fields you can read without another round-trip:
dlg.hwnd # int
dlg.pid # int
dlg.proc # str, process name
dlg.title # current window title
dlg.as_dict() # {hwnd, pid, proc, title, rect}dlg = gui.find(title_contains="Login", timeout_s=5)
if dlg:
dlg.send_text("alice", into="Username")
dlg.send_text("secret", into="Password")
dlg.click("OK")
_result = {"dismissed": dlg is not None}dlg = gui.find(title_contains="Question", timeout_s=3)
if dlg is None:
dlg = gui.find(title_contains="overwrite", timeout_s=3) # other
if dlg:
dlg.click("OK")
_result = {"confirmed": dlg is not None, "title": dlg.title if dlg else None}state = gui.snapshot(max_depth=4)
names = []
def walk(items):
for c in items:
if c.get("name"):
names.append((c["control_type"], c["name"]))
walk(c.get("children") or [])
for w in state["windows"]:
walk(w.get("controls") or [])
_result = {"control_names": names[:50]}dlg = gui.find(title_contains="Main", timeout_s=3)
if dlg:
shot = dlg.screenshot(label="after_solve")
_result = shot # contains {ok, path, width, height}
else:
_result = {"ok": False, "error": "main window not found"}Every call returns a dict; failures look like
{"ok": False, "error": "connect(handle=...) failed: ..."}. The UIA
machinery runs in an isolated subprocess so a COM glitch in one call
never poisons the next.
Things that commonly make ok false:
| Symptom | Likely cause | What to do |
|---|---|---|
find returns None | title didn't match / process filter too strict | print gui.list_windows() to see what is live |
click says no control titled ... in hwnd=... | the button label in the UI is not what you think | snapshot the window, read controls[*].name |
screenshot returns minimal PNG | window is minimized (pywinauto captures the window rect; min'd windows live at (-32000, -32000, …)) | dlg.activate() first, then screenshot |
gui.available is False | off-Windows host, or pywinauto not installed | don't use gui — fall back to SDK-only path |
list_windows() returns [] even though the solver clearly launched | sim serve was started from an SSH / non-interactive Windows session — the GUI exists in a session with no display surface and pywinauto can't see it | ask the operator to restart sim serve from a desktop session (RDP, Windows Terminal, or Task Scheduler with "run only when user is logged on" + interactive). See ../SKILL.md → "Where sim serve runs". Do not retry. |
screenshot returns a uniformly black PNG | same as above — non-interactive session has no compositor | same fix |
gui.snapshot() to confirm the actual accessible name before calling
click(name).find(title) returns the first match; if the workflow is
ambiguous, use list_windows() and pick by pid.gui for SDK-shaped work. Solver objects (session,
model) are always faster and more reliable than UI clicks. gui is
the fallback for the UI-only surface.sim serve runs from an
SSH session, a Windows service, or any non-interactive context, the
spawned solver process inherits a session with no display surface.
pywinauto then finds zero windows, screenshots come back uniformly
black, and find(...) silently times out — the server itself is up
and reachable, only the GUI half is dead. Restart sim serve from a
desktop session (Windows Terminal on the console, RDP, or Task
Scheduler with "run only when user is logged on" + interactive). See
../SKILL.md → "Where sim serve runs" for the full
driver-by-driver matrix.sim.inspect probes (issue #8a window_observed, #8b screenshots)
tell you what is on screen — read them first, then reach for
gui to act.© svd-ai-lab, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 1 other file in src/sim/_skills/sim-cli/gui of svd-ai-lab/sim-cli.
Open the folder on GitHubat commit b7318cc
Gui next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Gui this skillsvd-ai-lab/sim-cli | 231 | — | ~2.2k | Automated safety check: Pass | Apache-2.0 | |
| Cantera Ignition DelayK-Dense-AI/scientific-agent-skills | 48k | 2 repos | ~2.2k | Automated safety check: Pass | MIT | |
| AstropyzLanqing/codex-claude-academic-skills | 4.6k | 14 repos | ~2.9k | Automated safety check: Pass | BSD-3-Clause | |
| PymatgenzLanqing/codex-claude-academic-skills | 4.6k | 12 repos | ~5k | Automated safety check: Pass | MIT | |
| Weathertrpc-group/trpc-agent-go | 1.8k | 9 repos | ~591 | Automated safety check: Pass | Apache-2.0 | |
| Pymol VisualizationChatMol/ChatMol | 372 | — | ~1.2k | Automated safety check: Pass | MIT |
K-Dense-AI/scientific-agent-skills
Runs Cantera constant-volume or constant-pressure ignition simulations and reports temperature-based ignition delay with mechanism provenance and checks.
zLanqing/codex-claude-academic-skills
Comprehensive Python library for astronomy and astrophysics.
zLanqing/codex-claude-academic-skills
Materials science toolkit. An agent skill from zLanqing/codex-claude-academic-skills.
trpc-group/trpc-agent-go
Get current weather and forecasts via wttr.in or Open-Meteo.
ChatMol/ChatMol
Generate publication-quality molecular visualization images using PyMOL.
Muuuun/luxas
Write domain-authentic review articles that synthesize rather than stack.
svd-ai-lab/sim-cli
Cross-solver operating discipline for sim-cli workflows — tool choice, input classification, acceptance semantics, and escalation rules that apply across solvers.
Categories
Cross-driver GUI actuation for CAE solvers running under sim-cli. Gui is an agent skill from svd-ai-lab/sim-cli. Cross-driver GUI actuation for CAE solvers running under sim-cli.
Gui fits situations like: dismiss dialogs; capture window screenshots against GUI-capable driver windows through sim exec.
Run `npx skills add svd-ai-lab/sim-cli --skill gui -a claude-code`. Or copy the skill folder (src/sim/_skills/sim-cli/gui in svd-ai-lab/sim-cli) into .claude/skills/gui in your project. Claude Code loads it when a task matches its description.
Run `npx skills add svd-ai-lab/sim-cli --skill gui -a codex`. Or copy the skill folder (src/sim/_skills/sim-cli/gui in svd-ai-lab/sim-cli) into .agents/skills/gui in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add svd-ai-lab/sim-cli --skill gui -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gui, .gemini/skills/gui, .github/skills/gui and .opencode/skills/gui in your project.
Going by SKILL.md and its folder, Gui needs Python for the scripts in its folder. Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Gui is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.2k tokens (SKILL.md is roughly 8.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Gui: Cantera Ignition Delay (K-Dense-AI/scientific-agent-skills, 48k stars), Astropy (zLanqing/codex-claude-academic-skills, 4.6k stars), Pymatgen (zLanqing/codex-claude-academic-skills, 4.6k stars) and Weather (trpc-group/trpc-agent-go, 1.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
svd-ai-lab (a GitHub organization) maintains it in svd-ai-lab/sim-cli, which has 231 GitHub stars. The repository holds 2 skills in this directory. The repository was last updated on September 8, 2026.
Source: svd-ai-lab/sim-cli on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.